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Found 12 papers, 2 papers with code
Synergizing Spatial Optimization with Large Language Models for Open-Domain Urban Itinerary Planning
In this paper, we for the first time propose the task of Open-domain Urban Itinerary Planning (OUIP) for citywalk, which directly generates itineraries based on users' requests described in natural language.
Exploring Large Language Models for Human Mobility Prediction under Public Events
Accurate human mobility prediction for public events is thus crucial for event planning as well as traffic or crowd management.
Deep trip generation with graph neural networks for bike sharing system expansion
For station-based BSSs, this means planning new stations based on existing ones over time, which requires prediction of the number of trips generated by these new stations across the whole system.
Adapting Node-Place Model to Predict and Monitor COVID-19 Footprints and Transmission Risks
This article adapts this model to investigate whether and how node, place, and mobility would be associated with the transmission risks and presences of the local COVID-19 cases in a city.
Cross-Mode Knowledge Adaptation for Bike Sharing Demand Prediction using Domain-Adversarial Graph Neural Networks
Existing methods for bike sharing demand prediction are mostly based on its own historical demand variation, essentially regarding it as a closed system and neglecting the interaction between different transportation modes.
HGARN: Hierarchical Graph Attention Recurrent Network for Human Mobility Prediction
To address these issues, we present Hierarchical Graph Attention Recurrent Network (HGARN) for human mobility prediction.
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards
Route choice modeling is a fundamental task in transportation planning and demand forecasting.
Bike Sharing Demand Prediction based on Knowledge Sharing across Modes: A Graph-based Deep Learning Approach
Bike sharing is an increasingly popular part of urban transportation systems.
Joint Demand Prediction for Multimodal Systems: A Multi-task Multi-relational Spatiotemporal Graph Neural Network Approach
Despite some recent efforts, existing approaches to multimodal demand prediction are generally not flexible enough to account for multiplex networks with diverse spatial units and heterogeneous spatiotemporal correlations across different modes.
Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns
The results show that our proposed model outperforms existing deep learning models in all kinds of missing scenarios and the graph structure estimation technique contributes to the model performance.
NetTraj: A Network-based Vehicle Trajectory Prediction Model with Directional Representation and Spatiotemporal Attention Mechanisms
None of them is ideal, as the cell-based representation ignores the road network structures and the other two are less efficient in analyzing city-scale road networks.
Individual Mobility Prediction: An Interpretable Activity-based Hidden Markov Approach
Individual mobility is driven by demand for activities with diverse spatiotemporal patterns, but existing methods for mobility prediction often overlook the underlying activity patterns.
